PubMed چکیده/رکورد

Detection of Acute Hepatitis C Outbreaks in California Using SaTScan: Statewide Spatiotemporal Analysis.

استودیوی صوتی مقاله

پخش حرفه‌ای فارسی و انگلیسی

در حال بررسی نسخه‌های صوتی ذخیره‌شده…

صوت تولیدشده با هوش مصنوعی است. برای کاربرد علمی یا درمانی، متن و منبع اصلی را بررسی کنید.
خواندن هوشمند فارسی و انگلیسی در حال آماده‌سازی صداهای مرورگر…
تنظیم صدای طبیعی و سرعت

صداهایی که در نامشان «Natural»، «Neural» یا «Online» دیده می‌شود معمولاً طبیعی‌ترند. انتخاب صدا به صداهای نصب‌شده در ویندوز و مرورگر شما بستگی دارد.

چکیده اصلی

BACKGROUND: Acute hepatitis C virus outbreaks in California are identified by local health jurisdictions through the investigation of cases reported by laboratories and health care providers to the state's public health surveillance system. However, acute hepatitis C cases are widely underreported, limiting timely outbreak detection and early intervention. SaTScan (Space and Time Scan Statistics) has been proposed as a valuable tool to better detect disease clusters that may be missed by traditional surveillance, even when the disease is underreported, but it is not routinely used for acute hepatitis C surveillance. Timely outbreak detection enables prompt investigation, interruption of transmission networks, and expeditious access to treatment that prevents chronic hepatitis C progression. OBJECTIVE: We assessed the feasibility of using SaTScan to identify verified acute hepatitis C outbreaks using public health surveillance data by conducting a retrospective cluster analysis followed by a prospective, proof-of-concept (POC) analysis that simulated routine surveillance at a single time point using the preceding 2 years of available data. METHODS: We geocoded acute hepatitis C cases with an episode date between January 2022 and December 2023 that were reported to the California Department of Public Health (CDPH). Cases among people experiencing homelessness were included using proxy locations corresponding to their reporting jurisdiction. First, we applied a retrospective space-time permutation scan to determine if any detected significant clusters corresponded to a verified acute hepatitis C outbreak reported in California. We then performed a single prospective POC scan using surveillance data from August 2020 through August 2022 to simulate routine surveillance on August 28, 2022. Detection performance was evaluated based on whether the scan generated a signal corresponding to the known acute hepatitis C outbreak, measured using recurrence intervals. RESULTS: Of the 236 acute hepatitis C cases reported in California with an episode date between January 2022 and December 2023, 97.9% (n=231) were successfully geocoded. The retrospective scan identified 1 significant cluster that corresponded to a verified outbreak in Los Angeles County. The prospective POC scan detected the same outbreak 1 day after the second outbreak-related case was reported, based on symptom onset. The cluster exceeded the recurrence interval threshold (1.7 y), and 2 of 3 outbreak-related cases were identified. CONCLUSIONS: SaTScan successfully identified a verified acute hepatitis C outbreak using both retrospective and prospective space-time permutation scans, demonstrating its potential to enhance real-time surveillance. However, detection performance depends on the timeliness and completeness of case reporting. Future research should explore the prospective use of SaTScan with real-time surveillance data to fine-tune signaling thresholds and scan statistic parameters and assess its broader applicability for acute hepatitis C outbreak detection.

متن کامل اصلی

متن در JumpToDate ذخیره نشده است.

برای بررسی دسترسی کتابخانه‌ای یا خرید، رکورد اصلی را باز کنید.

رفتن به منبع اصلی

کلیدواژه‌ها

POCSaTScanSpace and Time Scan Statisticshepatitis Coutbreak detectionproof-of-conceptpublic health surveillancespace-time permutation
در همین زیرشاخه

مقاله‌های مرتبط

PubMed2026

The Impact of Infectious Disease Severity on Stigma Associated With Affected Regions: A Facial Representation Perspective.

Individuals from regions affected by infectious disease outbreaks may be stigmatized because others hold negative mental images-or mental representations-of them. This study investigated whether such outbreaks lead to the formation of negative mental representations of residents in affected areas and explored the role of disease severity in this process. Study 1 revealed that during the early stages of the COVID-19 epidemic, the public…

PubMed2026

Impact of Community Factors on Mental and Physical Health During Successive Waves of COVID-19 Pandemic in China.

This study examined how community factors impacted well-being during the COVID-19 pandemic in China. Data were collected at two time points: Time 1 (T1) in September 2021 (N = 992) when COVID-19 cases stabilized, and Time 2 (T2) in January 2023 (N = 497) during a sudden outbreak of new infected cases. At T1, perceived community safety and sense of community directly affected depression, anxiety, and physical health symptoms, with sense…

PubMed2026

Narrative Numbing and the Politics of Public Health Emergencies.

We examine how political polarization and prolonged crisis governance contribute to narrative numbing in public health emergencies and explore how these dynamics reshape executive emergency powers in the United States. Using Google Trends data, we assess changes in public attention to the opioid epidemic. We follow this with an explanatory case study of Pennsylvania's opioid state of emergency and a comparative review of national legis…

PubMed2026

Psychoeducational Challenges of Dyslexic Children During the COVID-19 Pandemic: A Systematic Review.

Dyslexic students faced psychological and educational challenges during the COVID-19 pandemic and online learning, including increased anxiety, low self-esteem, reduced reading interest, limited access to educational technologies, insufficient teacher support, and difficulties with reading fluency and concentration. Examining these challenges is crucial for implementing effective measures and enhancing post-pandemic learning. This stud…